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Incorporating physical dependencies into gas source localization allows for accurate inference even in challenging indoor environments, outperforming traditional methods.
Self-supervised learning can transform noisy LiDAR SLAM into a robust system by recursively refining local geometric representations.
Real-time, uncertainty-aware signed distance functions are now possible, thanks to a new kernel regression and Gaussian process hybrid that outperforms existing methods in accuracy and speed.
